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Kenneth Chaney

8 accepted papers

2025

EvMAPPER: High-Altitude Orthomapping with Event Cameras

ICRA 2025

Traditionally, unmanned aerial vehicles (UAVs) rely on CMOS-based cameras to collect images about the world below. One of the most successful applications of UAVs is to generate orthomosaics or orthomaps, in which a series of images are integrated to develop a larger map. However, using CMOS-based c

Cited by 3SourceScholar
2024

Motion-prior Contrast Maximization for Dense Continuous-Time Motion Estimation

ECCV 2024poster

"Current optical flow and point-tracking methods rely heavily on synthetic datasets. Event cameras are novel vision sensors with advantages in challenging visual conditions, but state-of-the-art frame-based methods cannot be easily adapted to event data due to the limitations of current event simula…

2022

EvAC3D: From Event-Based Apparent Contours to 3D Models via Continuous Visual Hulls

ECCV 2022poster

"3D reconstruction from multiple views is a successful computer vision field with multiple deployments in applications. State of the art is based on traditional RGB frames that enable optimization of photo-consistency cross views. In this paper, we study the problem of 3D reconstruction from event-c…

2021

Self-Supervised Optical Flow with Spiking Neural Networks and Event Based Cameras

IROS 2021poster

Optical flow can be leveraged in robotic systems for obstacle detection where low latency solutions are critical in highly dynamic settings. While event-based cameras have changed the dominant paradigm of sending by encoding stimuli into spike trails, offering low bandwidth and latency, events are s…

Cited by 19SourceScholar
2020

Spike-FlowNet: Event-based Optical Flow Estimation with Energy-Efficient Hybrid Neural Networks

ECCV 2020poster

Event-based cameras display great potential for a variety of tasks such as high-speed motion detection and navigation in low-light environments where conventional frame-based cameras suffer critically. This is attributed to their high temporal resolution, high dynamic range, and low-power consumptio…

2019

Unsupervised Event-Based Learning of Optical Flow, Depth, and Egomotion

CVPR 2019poster

In this work, we propose a novel framework for unsupervised learning for event cameras that learns motion information from only the event stream. In particular, we propose an input representation of the events in the form of a discretized volume that maintains the temporal distribution of the events…

Cited by 648PDFScholar
2018

EV-FlowNet: Self-Supervised Optical Flow Estimation for Event-based Cameras

RSS 2018poster

Event-based cameras have shown great promise in a variety of situations where frame based cameras suffer, such as high speed motions and high dynamic range scenes. However, developing algorithms for event measurements requires a new class of hand crafted algorithms. Deep learning has shown great suc…